bert-base-uncased-News_About_Gold
This model is a fine-tuned version of bert-base-uncased. It achieves the following results on the evaluation set:
- Loss: 0.2995
- Accuracy: 0.9139
- Weighted f1: 0.9133
- Micro f1: 0.9139
- Macro f1: 0.8758
- Weighted recall: 0.9139
- Micro recall: 0.9139
- Macro recall: 0.8647
- Weighted precision: 0.9131
- Micro precision: 0.9139
- Macro precision: 0.8885
Model description
For more information on how it was created, check out the following link: https://github.com/DunnBC22/NLP_Projects/blob/main/Sentiment%20Analysis/Sentiment%20Analysis%20of%20Commodity%20News%20-%20Gold%20(Transformer%20Comparison)/News%20About%20Gold%20-%20Sentiment%20Analysis%20-%20Bert-Base%20with%20W%26B.ipynb
This project is part of a comparison of seven (7) transformers. Here is the README page for the comparison: https://github.com/DunnBC22/NLP_Projects/tree/main/Sentiment%20Analysis/Sentiment%20Analysis%20of%20Commodity%20News%20-%20Gold%20(Transformer%20Comparison)
Intended uses & limitations
This model is intended to demonstrate my ability to solve a complex problem using technology.
Training and evaluation data
Dataset Source: https://www.kaggle.com/datasets/ankurzing/sentiment-analysis-in-commodity-market-gold
Input Word Length:
Class Distribution:
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Weighted f1 | Micro f1 | Macro f1 | Weighted recall | Micro recall | Macro recall | Weighted precision | Micro precision | Macro precision |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0.6809 | 1.0 | 133 | 0.3347 | 0.8974 | 0.8928 | 0.8974 | 0.8043 | 0.8974 | 0.8974 | 0.7710 | 0.8966 | 0.8974 | 0.8800 |
| 0.2778 | 2.0 | 266 | 0.2844 | 0.9144 | 0.9145 | 0.9144 | 0.8668 | 0.9144 | 0.9144 | 0.8689 | 0.9150 | 0.9144 | 0.8650 |
| 0.2022 | 3.0 | 399 | 0.2823 | 0.9172 | 0.9166 | 0.9172 | 0.8820 | 0.9172 | 0.9172 | 0.8691 | 0.9165 | 0.9172 | 0.8968 |
| 0.1496 | 4.0 | 532 | 0.2856 | 0.9167 | 0.9164 | 0.9167 | 0.8838 | 0.9167 | 0.9167 | 0.8686 | 0.9167 | 0.9167 | 0.9027 |
| 0.1196 | 5.0 | 665 | 0.2995 | 0.9139 | 0.9133 | 0.9139 | 0.8758 | 0.9139 | 0.9139 | 0.8647 | 0.9131 | 0.9139 | 0.8885 |
Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0
- Datasets 2.11.0
- Tokenizers 0.13.3
License Notice
This model is a fine-tuned derivative of a pretrained model. Users must comply with the original model license.
Dataset Notice
This model was fine-tuned on third-party datasets which may have separate licenses or usage restrictions.